Implementation of statistical methods for the modeling and changepoint detection in time series of counts, proportions and categorical data, as well as for the modeling of continuoustime epidemic phenomena, e.g., discretespace setups such as the spatially enriched SusceptibleExposedInfectiousRecovered (SEIR) models, or continuousspace point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LRCUSUM method described in Höhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided. Continuous selfexciting spatiotemporal point processes are modeled through additivemultiplicative conditional intensities as described in Höhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space). The package contains several realworld data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatiotemporal fashion.
Package details 


Maintainer  
License  GPL2 
Version  1.100 
URL  http://surveillance.rforge.rproject.org/ 
Package repository  View on GitHub 
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